Multi-channel imaging in MRI has been extremely successful in enabling the ability to reconstruct high quality images despite subsampling. However, the use of parallel imaging algorithms for reconstruction are confined by the need to acquire an extra calibration scan. Alternatively, the efficiency of data sampling is compromised to properly acquire sufficient data for calibration. This calibration procedure is prone to error, and is time consuming to perform for pseudo-random sampling schemes that are ideal for advanced reconstruction methods.
High-density receiver coil arrays are highly advantageous for magnetic resonance imaging (MRI). These hardware setups increase signal-to-noise-ratio by reducing the body noise. Additionally, the localized sensitivity profile of each array element can be exploited in the image reconstruction. The additional information from different sensitivity profiles enables image recovery despite subsampling the data acquisition. This feature allows for a reduction factor of 2 to 8 in scan durations. The scan time acceleration reduces the amount of patient motion, increases the temporal and/or spatial resolution for dynamic imaging, and overall improves patient experience.
The image reconstruction algorithms that enable this scan time reduction using the coil sensitivity information are collectively known as “parallel imaging,” and they can be applied in either the frequency domain or the image domain. Popular frequency domain (i.e., k-space) parallel imaging algorithms include GRAPPA and SPIRIT. Popular image-based algorithms include SENSE and ESPIRiT. These standard techniques rely on calibration data (i.e., fully sampled low-resolution image) that characterize the localized sensitivity profiles to guide the image reconstruction. This procedure is followed by a k-space interpolation step that leverages the calibration information to recover missing k-space samples. The calibration data can be obtained from a low-resolution image. However, to collect the calibration data, an additional MRI sequence must be used as a calibration scan. Alternatively, the data sampling must be fully sampled in the center of the k-m space for an auto-calibrated approach. The calibration scan prolongs the overall MRI exam time and is prone to error from mis-registration caused by patient motion between the calibration and imaging scans.
Auto-calibrated approaches (i.e., where acquisition of a low resolution image is part of the current scan) address these issues but impose a constraint on the k-space sampling. For high undersampling factors (>4), most of the data acquisition is spent on acquiring the auto-calibration region; this limitation decreases the sampling of high-spatial frequencies and compromises the ability to recover high spatial resolution information. Moreover, the computation time needed to estimate the information from the sensitivity maps can exceed the actual time for applying the information. A number of calibration-less algorithms (i.e., where no fully sampled low resolution data is available) have been recently proposed that address a few of these issues. Many of these methods rely on a lower dimensional subspace in the k-space samples to recover the missing data samples. These methods rely heavily on iterative algorithms, are time consuming to perform, and have challenges of ensuring convergence with good quality resulting images. These types of algorithms are much longer than standard parallel imaging algorithms.